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ALIS: Autonomous Learning in Intelligent System

Abstract

Intelligent systems are machines that have their own goals, perceive, respond and learn based on their experiences. Reinforcement Learning (RL) is a powerful tool for this purpose because the system autonomously learns a policy, through trial and error in repeated interactions with the environment. This project seeks to increase the dissemination of the RL technology and advance the frontiers of knowledge of the self-learning area. However, many challenges must still be overcome in order to have a broad use of RL in intelligent systems. Challenges include dealing with uncertainties of sensors and actuators, a dynamic world that changes continuously and requires quick decisions, continuous quantities and the high RL computational cost. Therefore, this scientific research project aims toinvestigate, propose, develop, and evaluate models and methods to make efficient and effective RL in intelligent systems that solve complex problems. In particular, it explores: (I) relational and object-oriented models and algorithms, opening opportunities for generalization in the representation and solution of complex problems; (Ii) distribution and division of workload among several apprentices agents; (Iii) appropriate approximation functions to represent both situations observed by the agent and the knowledge acquired; (Iv) knowledge transfer so that the knowledge acquired byanother agent or after the learning of previous tasks can be reused to accelerate the learning of a new similar task. From the point of view of applications, this project aims to implement and evaluate models and algorithms in areas such as games, robotics, computational biology, among others. (AU)

Scientific publications (8)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
GLATT, RUBEN; DA SILVA, FELIPE LENO; DA COSTA BIANCHI, REINALDO AUGUSTO; REALI COSTA, ANNA HELENA. DECAF: Deep Case-based Policy Inference for knowledge transfer in Reinforcement Learning. EXPERT SYSTEMS WITH APPLICATIONS, v. 156, OCT 15 2020. Web of Science Citations: 0.
DA SILVA, FELIPE LENO; GLATT, RUBEN; REALI COSTA, ANNA HELENA. MOO-MDP: An Object-Oriented Representation for Cooperative Multiagent Reinforcement Learning. IEEE TRANSACTIONS ON CYBERNETICS, v. 49, n. 2, p. 567-579, FEB 2019. Web of Science Citations: 2.
DA SILVA, FELIPE LENO; REALI COSTA, ANNA HELENA. A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems. JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, v. 64, p. 645-703, 2019. Web of Science Citations: 2.
BIANCHI, REINALDO A. C.; SANTOS, PAULO E.; DA SILVA, ISAAC J.; CELIBERTO, JR., LUIZ A.; DE MANTARAS, RAMON LOPEZ. Heuristically Accelerated Reinforcement Learning by Means of Case-Based Reasoning and Transfer Learning. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, v. 91, n. 2, SI, p. 301-312, AUG 2018. Web of Science Citations: 1.
PERICO, DANILO H.; HOMEM, THIAGO P. D.; ALMEIDA, AISLAN C.; SILVA, ISAAC J.; VILAO, JR., CLAUDIO O.; FERREIRA, VINICIUS N.; BIANCHI, REINALDO A. C. Humanoid Robot Framework for Research on Cognitive Robotics. JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS, v. 29, n. 4, p. 470-479, AUG 2018. Web of Science Citations: 0.
PERAFAN VILLOTA, JUAN CARLOS; DA SILVA, FELIPE LENO; JACOMINI, RICARDO DE SOUZA; REALI COSTA, ANNA HELENA. Pairwise registration in indoor environments using adaptive combination of 2D and 3D cues. Image and Vision Computing, v. 69, p. 113-124, JAN 2018. Web of Science Citations: 0.
FERREIRA, LEONARDO A.; BIANCHI, REINALDO A. C.; SANTOS, PAULO E.; LOPEZ DE MANTARAS, RAMON. Answer set programming for non-stationary Markov decision processes. APPLIED INTELLIGENCE, v. 47, n. 4, p. 993-1007, DEC 2017. Web of Science Citations: 1.
JACOMINI, RICARDO DE SOUZA; MARTINS, JR., DAVID CORREA; DA SILVA, FELIPE LENO; REALI COSTA, ANNA HELENA. GeNICE: A Novel Framework for Gene Network Inference by Clustering, Exhaustive Search, and Multivariate Analysis. JOURNAL OF COMPUTATIONAL BIOLOGY, v. 24, n. 8, p. 809-830, AUG 2017. Web of Science Citations: 0.

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